Interacting with computers is not limited to mouse and keyboard. Sensing the movement of a person to recognize his/her gesture is the subject of a wide spectrum of research in Human Computer Interaction and Computer Vision. Recognizing human hand gestures in particular provides computers with a natural method of communication. Applications from medical to surveillance and security may use the technology described herein. Learning and recognizing hand movements are significant components of such technologies.
Bimanual movements in general form a large subset of hand movements in which both hands move simultaneously in order to do a task or imply a meaning. Clapping, opening a bottle, typing on a keyboard and drumming are some common bimanual movements. Sign Languages also use bimanual movements to accommodate sets of gestures for communication.
Typically, a prerequisite to recognition of hand movements is tracking. Objects may be tracked using stereo imaging.
Two common techniques used in tracking are Kalman filtering and Particle filtering. Particle filtering may be used for tracking and resolving occlusion problems. Other tracking algorithms may use techniques such as, for example, Bayesian Networks, object model matching based on probabilistic tracking functions, minimization of cost functions, and analytic model matching. Several tracking algorithms include non-linear optimizations.